Cluster configurations of the Hegselmann-Krause model on network ensembles

<p>This is the raw data underlying the results of the preprint [arxiv:2102.10910](https://arxiv.org/abs/2102.10910).</p> <p>&nbsp;</p> <p>## Data</p> <p>For each measured combination of the confidence and system size, there is one gzipped<br> file. For different ensembles, we collected data in different ranges and quality.<br> The paramters are:</p> <p>* Number of samples `m` per parameter combination<br> * Range `r` of confidences epsilon<br> * Distances `d` between values of epsilon (basically the resolution of the data)<br> * Largest size `N_max`</p> <p>The single files follow a naming scheme of `n{N}_e{epsilon}.cluster.dat.gz`, where<br> `{N}` signals the system size of the simulation and `{epsilon}` is the confidence<br> value of the simulation (without a decimal point, i.e., `0050` corresponds to `epsilon = 0.050`).<br> The sizes `N` are usually powers of two (or for the lattices, perfect squares close to powers of two).</p> <p>We present the data for each ensemble in one archive.</p> <p><br> * Fully connected `full.tar`<br> &nbsp;&nbsp;&nbsp; * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 262144`<br> * Barabasi Albert with a mean degree of 4 `BA4.tar`<br> &nbsp;&nbsp;&nbsp; * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 32768`<br> * Barabasi Albert with a mean degree of 10 `BA10.tar`<br> &nbsp;&nbsp;&nbsp; * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with first nearest neighbors `lat1.tar`<br> &nbsp;&nbsp;&nbsp; * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`<br> * Square lattice with second nearest neighbors `lat2.tar`<br> &nbsp;&nbsp;&nbsp; * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`<br> * Square lattice with third nearest neighbors `lat3.tar`<br> &nbsp;&nbsp;&nbsp; * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with fourth nearest neighbors `lat4.tar`<br> &nbsp;&nbsp;&nbsp; * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br> * Square lattice with third nearest neighbors and 1% rewired edges `lat3_ws.tar`<br> &nbsp;&nbsp;&nbsp; * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.001`, `N_max = 16384`<br> * connected Erdos Renyi with mean degree of 10 `ER10.tar`<br> &nbsp;&nbsp;&nbsp; * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.002`, `N_max = 32768`</p> <p>&nbsp;</p> <p>## Data format</p> <p>Each final state is encoded as three lines:</p> <p>* The convergence time is a single integer with a line prefix &#39;# sweeps: &#39;<br> * The positions of all clusters in opinion space with a line prefix &#39;# &#39; (unsorted)<br> * The number of agents in each of the clusters without a line prefix</p> <p>&nbsp;</p> <p>## Python example for reading the format</p> <p>An example script, which visualizes the S vs eps graph for the largest size of the fully connected<br> case, with a function to read this format is given in `example.py`.</p>